{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable transformers : LogTransformer\n",
    "\n",
    "The LogTransformer() applies the natural logarithm or the base 10 logarithm to\n",
    "numerical variables. The natural logarithm is logarithm in base e.\n",
    "\n",
    "The LogTransformer() only works with numerical non-negative values. If the variable\n",
    "contains a zero or a negative value the transformer will return an error.\n",
    "\n",
    "**For this demonstration, we use the Ames House Prices dataset produced by Professor Dean De Cock:**\n",
    "\n",
    "Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing\n",
    "Data as an End of Semester Regression Project, Journal of Statistics Education, Vol.19, No. 3\n",
    "\n",
    "http://jse.amstat.org/v19n3/decock.pdf\n",
    "\n",
    "https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889627\n",
    "\n",
    "The version of the dataset used in this notebook can be obtained from [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from feature_engine.imputation import ArbitraryNumberImputer\n",
    "from feature_engine.transformation import LogTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "\n",
    "data = pd.read_csv('houseprice.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 79), (438, 79))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['Id', 'SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distributions before transformation\n",
    "\n",
    "X_train['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distributions before transformation\n",
    "\n",
    "X_train['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log base e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogTransformer(variables=['LotArea', 'GrLivArea'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialzing the tansformer with log base e\n",
    "\n",
    "lt = LogTransformer(variables=['LotArea', 'GrLivArea'], base='e')\n",
    "\n",
    "lt.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotArea', 'GrLivArea']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variables that will be transformed\n",
    "\n",
    "lt.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the log transform\n",
    "\n",
    "train_t = lt.transform(X_train)\n",
    "test_t = lt.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable distribution\n",
    "\n",
    "train_t['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable distribution\n",
    "\n",
    "train_t['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return variables to original representation\n",
    "\n",
    "train_orig = lt.inverse_transform(train_t)\n",
    "test_orig = lt.inverse_transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automatically select numerical variables\n",
    "\n",
    "The transformer will transform all numerical variables if no variables are specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load numerical variables only\n",
    "\n",
    "variables = ['LotFrontage', 'LotArea',\n",
    "             '1stFlrSF', 'GrLivArea',\n",
    "             'TotRmsAbvGrd', 'SalePrice']\n",
    "\n",
    "data = pd.read_csv('houseprice.csv', usecols=variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1022, 5), (438, 5))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's separate into training and testing set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.drop(['SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0)\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute missing values\n",
    "\n",
    "arbitrary_imputer = ArbitraryNumberImputer(arbitrary_number=2)\n",
    "\n",
    "arbitrary_imputer.fit(X_train)\n",
    "\n",
    "# impute variables\n",
    "train_t = arbitrary_imputer.transform(X_train)\n",
    "test_t = arbitrary_imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogTransformer(base='10')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform all numerical variables with base 10\n",
    "\n",
    "lt = LogTransformer(base='10', variables=None)\n",
    "\n",
    "lt.fit(train_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'LotArea', '1stFlrSF', 'GrLivArea', 'TotRmsAbvGrd']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variables that will be transformed\n",
    "\n",
    "lt.variables_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GrLivArea')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before transformation\n",
    "train_t['GrLivArea'].hist(bins=50)\n",
    "plt.title('GrLivArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'LotArea')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X8DXgGOC6zLyn14/DUdzSmSOsTzNr08zaNJtVten5G6qSpOnnT6hKUoUMd0mq0KwM97n06w0iYm9E7ImIuyJiZ2lbFBHfiIgHyteTSntExDWlLrsj4uy246wv4x+IiPVt7eeU4w+XfWPqz3JiIuK6iDgQEXe3tU16LZoeYyZpqM1VEbG/PHfuiojVbX1XlPO8PyLe3NY+5murfEDiztJ+Q/mwBBFxfNkeLv39U3TKExYRyyPi9oi4NyLuiYj3lPa6nzuZOasWWm/SPgi8FDgO+D5wxnTPaxLPdy+weFTbPwKbyvom4CNlfTVwKxDASuDO0r4IeKh8Pamsn1T6vl3GRtn3guk+53Fq8TrgbODuqaxF02PMpKWhNlcBfzXG2DPK6+Z44PTyejpmvNcWcCNwSVn/FPAXZf0vgU+V9UuAG6a7FmOc7xLg7LL+YuC/Sw2qfu5Me+GP4B/q1cDX2ravAK6Y7nlN4vnu5YXhfj+wpKwvAe4v658G3jZ6HPA24NNt7Z8ubUuAH7S1P2/cTFyA/lEBNum1aHqMmbaMUZurGDvcn/eaofXJtlc3vbZKYD0BzCvtvxo3sm9Zn1fGxXTXokOdbqL1u6+qfu7MxtsyS4Eftm3vK221SuDrEbErWr+yAaAvMx8t648BfWW9qTbjte8bo302mYpaND3GbPCucmvhurZbAt3W5mTgqcx8blT7845V+g+V8TNSuW30SuBOKn/uzMZwn2tem5lnAxcAl0XE69o7s3VJ4OdZmZpazLJ6fxL4beAs4FHgn6d1NtMsIuYDXwLem5lPt/fV+NyZjeE+p369QWbuL18PAF+h9Vs3H4+IJQDl64EyvKk247UvG6N9NpmKWjQ9xoyWmY9n5i8y85fAv9J67kD3tXkSWBgR80a1P+9YpX9BGT+jRMSxtIL9s5n55dJc9XNnNob7nPn1BhFxYkS8eGQdeBNwN63zHXmnfj2te4iU9nXl3f6VwKHyX8KvAW+KiJPKf83fROue6aPA0xGxsry7v67tWLPFVNSi6TFmtJFQKf6I1nMHWudzSfmky+nAClpvCI752ipXnLcDF5f9R9d5pDYXA7eV8TNG+fe8FrgvMz/a1lX3c2e639w4wjdEVtN6x/tB4Mrpns8knudLaX1i4fvAPSPnSuue5g7gAeC/gEWlPWj9oZQHgT3AQNux/hQYLss729oHaL3oHwT+hRn8ZhjweVq3F/6X1n3NDVNRi6bHmElLQ20+U859N62QWdI2/spynvfT9gmpptdWeS5+u9TsC8Dxpf2Esj1c+l863bUYozavpXU7ZDdwV1lW1/7c8dcPSFKFZuNtGUlSB4a7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqtD/Ad3CE2QH5lb0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Before transformation\n",
    "train_t['LotArea'].hist(bins=50)\n",
    "plt.title('LotArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the data\n",
    "\n",
    "train_t = lt.transform(train_t)\n",
    "test_t = lt.transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GrLivArea')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "\n",
    "train_t['GrLivArea'].hist(bins=50)\n",
    "plt.title('GrLivArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'LotArea')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transformed variable\n",
    "train_t['LotArea'].hist(bins=50)\n",
    "plt.title('LotArea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return variables to original representation\n",
    "\n",
    "train_orig = lt.inverse_transform(train_t)\n",
    "test_orig = lt.inverse_transform(test_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['LotArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AD7VPLvcB17ajMTYD5wHfXpGdGIDM/ExmbsrMCTpfSj6Umdcx4nVZkmF/i1rxBnwVOAT8jM582w105uIeBJ4B/gk4o20bdH744llgHzA553V+m84XLgeATwx7v/pQl1+jMz3yXeDxdvvwqNcG+GXgsVaXJ4A/b+3voBM0B4CvAae09lPb+oH2+DvmvNaftXo9DXxo2PvWxxpN8eZRKNalx5un0ktSUU6hSFJRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JR/w+dfRazql6e0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inverse transformed variable distribution\n",
    "\n",
    "train_orig['GrLivArea'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fenotebook",
   "language": "python",
   "name": "fenotebook"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
